The advent of big data, i.e., the ability to access a previously unimaginable amount of data - sometimes in real-time - opens up many new fields of activity for companies. In connection with new technologies, this data becomes the driver of the information revolution.
In the financial sector, in particular, companies cannot miss opportunities that result from this development or must be used to remain competitive at all. But big data information can also be used efficiently for financial regulatory issues, particularly for anti-money laundering issues. Big data technologies are now part of the standard repertoire for control and monitoring tasks by AML authorities.
The following article aims to explain the opportunities and risks of digitization on the topic of big data in the context of the current development of global AML regulations.
Digitization and Big Data
There is no doubt that the financial sector is in a phase of change, which will be shaped by stricter regulatory requirements and be decisively transformed by technological progress. Digitalization is now finding its way into all business areas of the financial institutions' cosmos and will fundamentally change today's conventional organizational structures of financial institutions.
In the spectrum of compliance activities, in addition to the digitization or automation of audit activities, the evaluation of huge data populations (i.e., "Big Data") plays an increasingly important role in tracking activities, showing developments, and thus making regulatory risks visible.
The definition of "Big" refers to the three "V" dimensions.
- Volume (scope, data volume)
- Velocity (the speed at which the data volumes are generated and transferred)
- Variety (range of data types and sources)
Technical solutions that obtain information from big data volumes must take into account the dimensions mentioned above.
Big data and AML issues
It was only a matter of time before AML regulatory bodies turned their attention to information that comes from financial institutions, digital data volumes - i.e., big data populations Findings gained from different types of data should show strategic market developments and risks at a glance.
In the regulatory context, data populations provide information about customers, products, and services that financial institutions can or should collect and analyze; Business activities and the associated AML risks increasingly have to be reported to authorities and audit firms in an easily legible manner.
The volume of available data populations has increased enormously in recent years due to automation, the digitalization of processes, and the introduction of artificial intelligence in various process chains.
Big Data as a "Big Opportunity"
Nowadays, big data or huge populations of data pose enormous challenges for companies in the financial services industry, but at the same time, offer lucrative opportunities. The ability to effectively collect, maintain, and analyze data can lead to better business strategy decisions and long-term competitive advantage Data has now become so important in the global economy that participants from the World Economic Forum in Davos in 2012 even declared data as a new class of economic goods - on a par with traditional assets such as currencies or gold.
The real challenge in the use of big data in business technology is, above all, to have a technical skillset and the right IT architecture that enables usable knowledge to be gained from a "forest of data." This is particularly difficult when data populations within an organization's IT environment come from different formats or from a wide variety of sources Findings from various types of reporting systems, dashboards, and complex decision models must, therefore, be able to be evaluated.
Regulators are aware that financial institutions have tons of data that can be used to identify compliance risks, conduct analysis, and remedy deficiencies. For their part, however, banks must-have technologies that make data readable, interpretable, and evaluable in large quantities.
Big Data Analytics Tools for Compliance Processes
Big data analytics platforms and tools offer solutions here to how risks or problems can be identified in real-time mode and potential breaches of rules avoided before inspection bodies and supervisory authorities discover them.
In this context, big data analytics solution models help, among other things:
- to better examine current regulatory rules, both in terms of their breadth and depth,
- to analyze data populations of transactions and customer segments across the board (and not - as previously - only in samples) and
- complying with the requirements regarding the supervisory authorities (e.g., requirements regarding the standardization of different data formats within the bank).
Classic vendor solutions from data analytics platforms and tools are, above all; initially, costly A financial institution must be ready to invest in financial resources. Financial institutions also need suitable experts who can handle data analytics platforms, both technically and from a regulatory and legal perspective Such multifunctional experts are rare and expensive Financial institutions must therefore evaluate or weigh up the cost-benefit effect of such investments, to what extent it wants to invest financial resources in technical compliance tools or, if necessary, in business strategy solutions Ultimately, financial institutions have to keep up with technological developments in analyzing big data as far as regulators are already using technology at a certain level.
The following section introduces the two classic compliance topics in which the use of big data analytics tools has become essential.
Possible Uses of Data Analytics in Compliance Departments
Countless regulations and paragraphs have been included in the national and international statutes in recent years and must be complied with accordingly by market participants. In the context of the electronic analysis of huge amounts of data to avoid regulatory violations, money laundering (regulations to combat corruption, bribery, robbery, extortion, drug trafficking, arms trafficking, or tax evasion ) can be identified:
Anti Money Laundering
Money laundering is the procedure for introducing illegally generated money or illegally acquired assets into the legal financial and economic cycle Since the money to be "washed" comes from illegal activities such as corruption, bribery, robbery, extortion, drug trafficking, arms trafficking, or tax evasion, its origin is to be obscured.
To combat money laundering, both national and international legal regulations have been established worldwide by regulators in the past 30 years. Financial institutions must, therefore, take various risks and rules into account when processing their monetary transactions.
In order to systematically monitor the huge amounts of data in money transactions carried out worldwide for information on money laundering, it is now standard for financial institutions to use software from the big data analytics spectrum that specializes in money laundering Factors such as the size of the transaction volume, the number of sales channels or the flow of assets across diverse geographical areas between thousands of market participants plays an important role.
Big data analytics programs to combat money laundering are said to be able to meet the following technical challenges in particular:
- Customer Due Diligence (CDD) and Know Your Customer (KYC): AML programs are intended to use customer information using external information sources ( check for clues to identify risky customer entities. Sanction Scanner enables businesses to comply with international regulations such as European Union, FATF, BSA, as well as national AML regulations. Sanction Scanner provides companies with global comprehensive and up-to-date AML data.
- Customer Onboarding & Monitoring Processes: Manuel's customer onboarding and customer monitoring processes may take too much time. Platforms with Artificial Intelligence ensure that financial institutions create AML and KYC compliant with customer onboarding and customer monitoring processes such as Sanction Scanner.
- Transaction Monitoring System (TMS): Rule-based TMS continuously monitors the transactions made by financial institutions. Such rules can include areas such as monetary thresholds or specific transaction patterns that indicate money laundering or corresponding deviations from regular transaction patterns.
- Individual customer profile monitoring: AML programs must monitor personal customer profiles based on behavior models. Individual behavioral patterns or indications of deviations from the corresponding behavioral pattern should be recognized, which could indicate potential money laundering.
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